How the intelligent electrical drivetrain will reduce the overall cost of energy

Matsinen Jari-PekkaABB Oy, Helsinki, Uusimaa, Finland

Abstract

This presentation describes how the electrical drivetrain in offshore wind turbines will evolve over the next few years. The main focus is on digitalization and how this will increase turbine uptime and reduce the overall cost of energy.

As wind turbines rated 8 MW+ become available, wind park developers will deploy their capital resources more efficiently by using fewer turbines to generate the required output. This means that uptime and overall performance will assume even greater significance, which in turn implies that predictive maintenance will play a crucial role in securing reliable turbine operation. Technologies that will enable advanced predictive maintenance include remote condition monitoring solutions (CMSs), smart sensing, cloud connectivity, and analytics.

At the same time intelligence built in to the electrical drivetrain – which includes the generator and converter - will allow the electrical drivetrains in a wind park to communicate. It will give them the capability to compensate for grid disturbances and faults in order to maintain output.

For example, if one electrical drivetrain in a wind park comes under severe stress and cannot provide its primary functionality without tripping, the other electrical drivetrains can prevent tripping by optimizing the wind park output and enabling the stressed electrical drivetrain to remain grid connected in a reduced run mode. As a result the energy yield is higher than if the stressed unit had been allowed to trip.

Remote CMSs are evolving, but commercialized solutions already exist. Smart sensing technology will cover typical parameters like temperatures and vibration (bearing problems, misalignment, and winding issues), but on-line cloud connectivity is in a rapid growth phase enabling analytics to evolve to the next level.

Analytics are presently limited to processing data based on smart sensors, but under development is a solution to boost the scope by storing real-time data from the converter directly into the cloud. This real-time data can be processed efficiently at microsecond intervals, enabling development of smart algorithms and analytics that will ensure faster and more predictive electrical drivetrain performance.

Results

The new technologies and use of remote CMSs will mean that maintenance can be planned in advance and preventive maintenance carried out instead of corrective maintenance, increasing turbine uptime. The resulting savings in maintenance, crane ships and man-hours will help to reduce the overall cost of energy.

New-generation analytics will provide numerous opportunities for optimizing wind park operations. For example, it may be possible to optimize turbine output by getting more active / reactive power out of an electrical drivetrain; greater flexibility in selecting the optimal operation point could enable reduction of losses in generators; and there could be new ways to avoid natural mode resonance frequencies in the generator or wind park, and even compensate power quality from the grid perspective.

Conclusions

Advanced predictive maintenance supported by augmented reality and intelligent, interconnected electrical drivetrains will provide wind park operators and service staff with the tools they need to perform safe field maintenance operations and to better manage the risks associated with having fewer but higher power turbines.

Analytics are currently being further developed to utilize raw data from the control system in converters, which is sampled at microsecond intervals. This provides major benefits over the present generation of analytics, which use data sampled at slower rates. Data from the wind park’s SCADA (supervisory control and data acquisition) system is sampled in multiples of seconds, and that from individual wind turbines’ PLCs (programmable logic controllers) is typically sampled at 1-100 millisecond intervals.